用python的regular expression做文字取代

https://docs.python.org/3/library/re.html#re.sub

把 \ 之前的文字全部刪除

re.sub(r'^\S* \\ ', '',the_string)

Gatsby製作Blog

https://www.gatsbyjs.org/docs/adding-a-list-of-markdown-blog-posts/
https://codinghero.netlify.com/

定時排程刪除超過一定天數的檔案

https://softwareengineering.stackexchange.com/questions/149824/automatically-delete-files-after-they-expire
https://superuser.com/questions/1072679/bash-what-does-means
http://linux.vbird.org/linux_basic/0430cron.php

find $search_path -atime +3 -exec rm {} \;

加到crontab裡面
# crontab -e

{}的意思就是find輸出的結果會帶到rm的參數，\;是確保command結束符號，可用+取代

pyenv-win 安裝與移除

https://github.com/pyenv-win/pyenv-win

1. Install
pip install pyenv-win --target %USERPROFILE%/.pyenv

2. Add following to PATH enviroment variable
%USERPROFILE%\.pyenv\pyenv-win\bin;%USERPROFILE%\.pyenv\pyenv-win\shims;

3. Add installed path in file easy_install.pth to site-packages folder(ex: C:\Users\Jack_Lin\AppData\Local\Programs\Python\Python37-32\Lib\site-packages).
Need to add absolute path, ex:
C:\Users\Jack_Lin\.pyenv in file easy_install.pth

4. pip list to check pyenv-win is installed and recognize by pip

5. upgrade
pip install --upgrade pyenv-win

6. uninstall
pip uninstall pyenv-win

如何push本地branch並覆蓋remote branch

git push -f --set-upstream origin master
git push -f  要小心使用

Scrapy學習筆記

若要整合Django和Scrapy，可利用DjangoItem，可節省定義Item的部分，讓Scrapy的Item定義自動去參照Django model，也可在Item增加欄位。

ItemLoader的部分，預設add_value後，postprocessor都會以list為最後輸出結果，可更感post processor為TakeFirst。

在Pipeline裡，若應用到DjangoItem，要先save後，在以Django model來操作 Foreign Key或是ManyToManyField部分。DjangoItem無法直接取得Django model內容。DjangoItem只是取去參考Django model，省去寫 field_xxx = scrapy.Field()而已。

pandas Dataframe操作

import pandas as pd
df = pd.read_excel('b.xlsx', 'Acceptance')
df[df['Config 3'].notna()]

裡面的df['Config 3'].notna()是filter的方式，結果是列出所有Config13欄位不是na的 rows

Pandas的Series是縱向的，例如某個Column的值的列表

pandas兩個主要的data structure
DataFrame和Series
DataFrame就是Series的容器
Series就是純量的容器

Indexing / Selection

The basics of indexing are as follows:

Operation	Syntax	Result
Select column	df[col]	Series
Select row by label	df.loc[label]	Series
Select row by integer location	df.iloc[loc]	Series
Slice rows	df[5:10]	DataFrame
Select rows by boolean vector	df[bool_vec]	DataFrame

如果要一個row一個row去做動作

https://stackoverflow.com/questions/16476924/how-to-iterate-over-rows-in-a-dataframe-in-pandas

df.iterrows()